The opportunities to put AI to work include high-level strategic decision-making, customer service, product development, marketing, cyber-security and much more.

Meanwhile, AI-based cyber-security tools introduce more advanced analysis of network traffic and unusual behavioral patterns for authentication and other activities. "It's important to identify what's normal behavior and what's abnormal behavior," states Chris O'Hara, principal in the Advisory Practice at PwC. "Today's firewalls are not strong enough to provide complete protection."

Getting Smart About AI

Like other digital technologies, AI potentially touches everything. So moving from a tactical approach to a more holistic strategic framework is essential, Accenture's Bianzino says. "In order to grasp what is possible, the technology people must understand the business concepts, and the business people must understand the IT concepts."

Communication and collaboration are critical. Neither side can achieve maximum results in a bubble; cross-pollination of ideas leads to better technology and process matches. What's more, AI initiatives increasingly involve a complex ecosystem that spans clouds, data repositories, APIs, and the actual AI software and algorithms that drive the results.

Bianzino advises organizations to experiment with AI and data—and learn as they go. "Everybody knows AI without data doesn't exist," he says, "but what often gets lost in the shuffle is the fact that you don't have to have perfect data to achieve results. You can learn and grow. You can experiment with data sets and spot opportunities as they arise."

This may translate into proof of concept and pilot projects. Some organizations also find it beneficial to establish innovation labs where data scientists and others can probe for opportunities and test hypotheses, and embrace open innovation frameworks that introduce contact points with outside entrepreneurs, accelerators and startups.

The key to success, Bianzino says, is understanding the basic business problem and determining how AI can intersect with value points. These may revolve around cost efficiencies; predictive analytics capabilities; understanding logistics and supply chain issues that would have been too complex to analyze in the past; using bots and automation to improve customer service; and making it easier for customers to order products by adding augmented reality (AR) features or more advanced image recognition features in a smartphone app.

Ultimately, it's also wise to approach AI with eyes wide open, Bianzino says. One of the remarkable and valuable things about AI is that it can lead to serendipitous discoveries and insights. Deep learning, machine learning and other AI systems may spot correlations and causalities that had previously flown below the enterprise radar.

AI systems may even find relationships between things that weren't within the scope of the original project. This may lead to new features in apps, revamped business processes, even entirely new products and services that redefine a business or an industry.

"We are moving into an era where AI will power everything," Bianzino point out. "So it must be part of the enterprise strategy."